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Your Quality Management Data Template

Veeva Vault Quality

Quality Management Attributes (22) Quality Management Activities (13) Extraction Guides (2)

Your Quality Management Data Template

This template provides a clear roadmap for collecting the essential data points you need to analyze your Quality Management processes. It outlines the crucial attributes, key activities, and offers practical guidance for extracting this information from Veeva Vault Quality, ensuring a smooth start to your process mining journey.

Recommended attributes to collect
Key activities to track
Extraction guidance

New to event logs? Learn how to create a process mining event log.

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Quality Management Attributes

These are the recommended data fields to include in your event log for a comprehensive analysis of your Quality Management process.

5 Required 8 Recommended 9 Optional

	Name	Description
	Quality Event QualityEvent	The unique identifier for a single quality event, such as a deviation, non-conformance, or CAPA.
Description The Quality Event serves as the primary case identifier, linking all activities, investigations, and resolutions related to a specific quality issue. It acts as the central thread that connects the entire lifecycle of an incident from its initial report to its final closure. In process mining, this attribute is crucial for reconstructing the end-to-end journey of each quality event. It allows for the analysis of process variations, cycle times, and compliance across different types of events, providing a comprehensive view of how individual quality incidents are managed, investigated, and resolved. Why it matters This is the essential Case ID that groups all related activities into a single process instance, making end-to-end analysis possible. Where to get This is the primary identifier for Quality Event records in Veeva Vault Quality, often referred to as 'Name' or a unique ID field on the Quality Event object. Examples QE-2023-00123CAPA-2023-00456DEV-2023-00789
	Activity Name ActivityName	The name of a specific task or step that occurred within the quality event lifecycle.
Description This attribute describes a distinct business activity or event that happens during the management of a quality event. These are the steps in the process, such as 'Investigation Initiated' or 'Corrective Action Plan Approved'. Analyzing the sequence and frequency of these activities is the core of process mining. It helps discover the actual process flow, identify bottlenecks between steps, and detect deviations from the standard operating procedure. Why it matters It defines the steps of the process, enabling the visualization and analysis of the process flow. Where to get This is typically derived from workflow task names, status changes, or audit trail entries within Veeva Vault Quality. Examples Initial Triage CompletedRoot Cause Analysis PerformedFinal Review And Closure
	Event Time EventTime	The timestamp indicating when a specific activity started or occurred.
Description This attribute captures the precise date and time that an activity was executed. It provides the chronological context for all events within a case. This timestamp is fundamental for all time-based process mining analyses. It is used to calculate cycle times, waiting times between activities, and durations of specific steps, which are essential for identifying delays and performance bottlenecks. Why it matters This timestamp is critical for ordering events chronologically and calculating all performance metrics like cycle time and waiting time. Where to get This corresponds to the creation date or completion date of tasks, or the timestamp of events in the audit trail of a Quality Event object. Examples 2023-10-26T10:00:00Z2023-11-01T14:35:10Z2023-11-15T09:21:05Z
	Last Data Update LastDataUpdate	The timestamp of the last data refresh or extraction from the source system.
Description This attribute indicates the freshness of the data being analyzed. It shows the date and time when the data was last extracted from Veeva Vault Quality and loaded into the process mining tool. This information is critical for users to understand the timeliness of the analysis and to know if the dashboards reflect the most current state of operations. It is a key data governance attribute. Why it matters Informs users about the timeliness and relevance of the data, ensuring they understand how current the process analysis is. Where to get This timestamp is generated and added during the data extraction, transformation, and loading (ETL) process. Examples 2024-05-20T08:00:00Z2024-05-21T08:00:00Z
	Source System SourceSystem	The system from which the quality management data was extracted.
Description This attribute identifies the origin of the data, which in this case is Veeva Vault Quality. It helps in data governance and provides context, especially when data from multiple systems is combined. In analysis, it's used for filtering and ensuring data lineage. Understanding the source system helps in interpreting the data correctly and troubleshooting any data quality issues. Why it matters Provides essential context about data origin, which is crucial for data governance, traceability, and multi-system analysis. Where to get This is a static value that should be added during the data transformation process to label all records from this source. Examples Veeva Vault Quality
	Activity Duration ActivityDuration	The processing time for a single activity.
Description This metric measures the time spent actively working on a task. It is calculated as the difference between the EndTime and StartTime of an activity. Analyzing activity duration helps pinpoint which specific steps in the process are most time-consuming. This differs from waiting time, as it reflects the actual work effort. It is a key metric for identifying inefficiencies within individual tasks and for resource capacity planning. Why it matters Measures the actual work time for tasks, helping to identify inefficient activities and opportunities for automation or simplification. Where to get This is a calculated metric, derived by subtracting the 'EventTime' (StartTime) from the 'EndTime' for each activity. Examples 2 hours 30 minutes45 minutes8 hours 15 minutes
	Assigned Department AssignedDepartment	The department or functional area responsible for the quality event or activity.
Description This attribute specifies the business unit or department, such as Quality Assurance, Manufacturing, or R&D, that is responsible for handling the quality event or a particular task. This is often derived from the assigned user's profile. This dimension is critical for high-level performance analysis. It allows for comparing process efficiency and compliance across different parts of the organization, helping to pinpoint systemic issues within specific departments and supporting the 'Investigator Workload Distribution' dashboard. Why it matters Allows for filtering and comparing process performance across different business units, revealing departmental bottlenecks or best practices. Where to get This information is typically stored in user profile data linked to the 'Assigned Investigator' or can be a direct field on the Quality Event object itself. Examples Quality AssuranceManufacturing OperationsResearch & Development
	Assigned Investigator AssignedInvestigator	The user or resource assigned to perform an investigation or a specific task.
Description This attribute identifies the person, role, or team responsible for executing a given activity within the quality event lifecycle. It can be the owner of the quality event itself or the assignee of a specific workflow task. Analyzing this attribute helps in understanding workload distribution, resource performance, and identifying overburdened teams or individuals. It is essential for the 'Investigator Workload Distribution' dashboard and for optimizing resource allocation to improve efficiency. Why it matters Tracks who performs the work, enabling analysis of workload, resource efficiency, and identification of training needs. Where to get Found in the owner or assigned user fields on the Quality Event object or its related workflow task objects in Veeva Vault Quality. Examples Alice JohnsonBob WilliamsQA Investigation Team
	End Time EndTime	The timestamp indicating when an activity was completed.
Description This attribute captures the date and time that a specific activity or task concluded. It is distinct from the StartTime (EventTime) and is essential for understanding the duration of individual steps in the process. In process mining, the EndTime is used in conjunction with the StartTime to calculate the processing time of activities. This is fundamental for identifying which specific tasks are taking the longest, contributing to overall cycle time and potential delays. Why it matters Enables the calculation of activity processing times, which is crucial for identifying bottlenecks at the task level. Where to get This timestamp is typically available in the workflow history or audit trail data for a Quality Event, often as a 'completed date' or 'modified on' field for a specific task or status. Examples 2023-10-26T11:30:00Z2023-11-01T18:00:10Z2023-11-15T09:45:00Z
	Quality Event Status QualityEventStatus	The current lifecycle state of the quality event.
Description This attribute indicates the current status of the quality event, such as 'Open', 'In Investigation', 'Pending Approval', or 'Closed'. It provides a snapshot of where each case is in its lifecycle. This is essential for the 'Quality Event Progress Overview' dashboard, enabling real-time monitoring of ongoing cases. It helps managers track progress, identify stalled events, and manage the overall workload effectively. Why it matters Provides a snapshot of the current state of a case, which is essential for monitoring ongoing work and identifying stalled events. Where to get This is a standard field on the Quality Event object in Veeva Vault Quality that reflects its position in the lifecycle. Examples In TriageIn InvestigationCAPA ProposedClosed
	Quality Event Type QualityEventType	The classification of the quality event, such as CAPA, Deviation, or Complaint.
Description This attribute categorizes the quality event based on its nature. Different types of quality events often follow different process paths and have different compliance requirements and resolution timelines. Analyzing by event type is fundamental to understanding process variations. It allows for comparing the performance and efficiency of handling deviations versus CAPAs, for example, and helps tailor improvement initiatives to specific process contexts. Why it matters Distinguishes between different kinds of quality processes, which often have unique workflows, SLAs, and compliance rules. Where to get This is a standard classification field on the Quality Event object in Veeva Vault Quality, often a picklist. Examples DeviationCorrective and Preventive Action (CAPA)Non-conformanceComplaint
	Severity Level SeverityLevel	The assessed severity or risk level of the quality event, such as high, medium, or low.
Description This attribute classifies the quality event based on its potential impact on product quality, patient safety, or regulatory compliance. The severity often dictates the urgency and required depth of the investigation. Analyzing by severity level is key to prioritizing improvement efforts. It helps understand if high-severity events take longer to resolve, follow different process paths, or have a higher rework rate, ensuring that the most critical issues receive the most attention. Why it matters Classifies events by impact, allowing for risk-based analysis and prioritization of process improvement efforts on high-risk areas. Where to get This is a standard classification field on the Quality Event object, often a picklist derived from a risk matrix. Examples HighMediumLow
	Target Resolution Date TargetResolutionDate	The planned or required date for the final closure of the quality event.
Description This attribute defines the service level agreement (SLA) or target deadline for resolving a quality event. It is often calculated based on the event's creation date and its severity or type. This date is the benchmark against which actual performance is measured. It is critical for the 'On-Time Resolution Performance' dashboard and the 'On-Time Resolution Rate' KPI, helping to identify whether events are being resolved in a timely manner and what factors contribute to delays. Why it matters Defines the SLA for case resolution, enabling the measurement of on-time performance and the analysis of delays. Where to get This is likely a date field on the Quality Event object, which may be manually entered or automatically calculated by the system. Examples 2024-01-15T23:59:59Z2024-02-28T23:59:59Z2024-03-10T23:59:59Z
	Approval Time ApprovalTime	The time taken for a specific approval activity to be completed.
Description This metric measures the duration of approval steps, such as 'Corrective Action Plan Approved'. It is calculated as the time from when an approval is requested to when it is granted or rejected. This attribute is specifically designed to support the 'Approval Workflow Bottlenecks' dashboard and the 'Approval Workflow Cycle Time' KPI. By isolating the time spent waiting for approvals, it helps identify and address delays caused by slow decision-making or inefficient approval processes. Why it matters Pinpoints delays in critical decision-making steps, helping to streamline approval workflows and reduce overall cycle time. Where to get This metric is calculated by finding the time difference between the start and end of approval-related activities in the event log. Examples 3 days 4 hours1 day 0 hours7 days 8 hours
	Cycle Time CycleTime	The total time elapsed from the creation of a quality event to its final closure.
Description This metric represents the end-to-end duration for resolving a quality event. It is calculated as the time difference between the very first activity (e.g., 'Quality Event Created') and the very last activity (e.g., 'Final Review and Closure'). Cycle time is a primary key performance indicator for overall process efficiency. It is used in the 'Quality Event Resolution Time' dashboard to analyze trends, identify factors that lead to long resolution times, and set benchmarks for process improvement. Why it matters Measures end-to-end process efficiency, providing a critical KPI to track overall performance and the impact of improvement initiatives. Where to get This is a calculated metric. It is computed at the case level by subtracting the timestamp of the first event from the timestamp of the last event. Examples 30 days 12 hours65 days 4 hours15 days 2 hours
	Effectiveness Check Result EffectivenessCheckResult	The outcome of the verification step to confirm if a corrective action was effective.
Description This attribute records the result of the effectiveness check, which is performed after a corrective action has been implemented. The outcome is typically 'Effective' or 'Not Effective'. This is a direct measure of the success of the CAPA process and is crucial for the 'CAPA Effectiveness & Recurrence Rate' dashboard and the 'CAPA Effectiveness Rate' KPI. It provides clear feedback on whether the implemented solutions are actually preventing the problem from happening again. Why it matters Directly measures the success of corrective actions, providing critical feedback for improving the problem-solving process. Where to get This would be a field, likely a picklist, on a CAPA Action or Effectiveness Check object related to the primary Quality Event. Examples EffectiveNot EffectivePending Verification
	Is On Time Resolution IsOnTimeResolution	A calculated flag indicating if the quality event was closed by its target date.
Description This boolean attribute is derived by comparing the actual closure date of a quality event with its 'Target Resolution Date'. It provides a simple, binary outcome for on-time performance. This flag is the foundation for the 'On-Time Resolution Performance' dashboard and the 'On-Time Resolution Rate' KPI. It allows for easy aggregation and filtering to understand what types of events, departments, or sites struggle to meet deadlines. Why it matters Provides a clear success or failure metric for meeting deadlines, simplifying performance analysis and reporting. Where to get Calculated by comparing the timestamp of the 'Final Review and Closure' activity with the 'TargetResolutionDate' attribute. Formula: Closure Time <= TargetResolutionDate. Examples truefalse
	Is Recurrence IsRecurrence	A flag indicating whether this quality event is a recurrence of a previously identified issue.
Description This boolean attribute signals that a quality event is not a new, unique issue but a repeat of a problem that has occurred before. This is a critical indicator of the failure of previous corrective actions. This flag is fundamental to the 'CAPA Effectiveness & Recurrence Rate' dashboard. Tracking recurrences provides direct feedback on the long-term success of the quality management system and helps identify systemic problems that are not being adequately addressed. Why it matters Highlights failures in corrective actions, enabling analysis of why problems are repeating and how to improve long-term solutions. Where to get This could be a checkbox field on the Quality Event object or a derived field based on links to previous, similar events. Examples truefalse
	Is Rework IsRework	A calculated flag that identifies activities or cases that represent rework.
Description This attribute is a boolean flag indicating that a particular activity or sequence of activities represents rework, such as repeating an investigation or re-opening a closed CAPA. It is not a standard field but is calculated based on process flow patterns. In process mining, this flag is used to quantify the amount of wasted effort and cost in the process. It is essential for the 'Rework Analysis' dashboard and the 'Quality Event Rework Rate' KPI, helping to pinpoint the causes of inefficiency and poor first-time quality. Why it matters Quantifies process inefficiency by flagging repeated work, which helps identify the root causes of quality issues and wasted effort. Where to get This attribute is not sourced directly. It is calculated during data transformation by detecting repeated activity names or loops in the process for a given case. Examples truefalse
	Product Product	The product or material associated with the quality event.
Description This attribute links the quality event to a specific product, material, or batch. This connection is vital in regulated industries for traceability and impact analysis. Analyzing quality events by product helps identify if certain products are more prone to issues, guiding product improvement or manufacturing process adjustments. It allows for filtering dashboards to see performance for specific product lines. Why it matters Links quality events to specific products, enabling analysis to identify product-specific issues and trends. Where to get This is typically a reference field on the Quality Event object that links to a Product or Material object in Veeva Vault. Examples Product A-100Product B-200Raw Material C-300
	Root Cause RootCause	The identified underlying cause of the quality event.
Description This attribute contains the final conclusion of the root cause analysis (RCA) investigation. It categorizes the fundamental reason for the quality issue, such as equipment failure, human error, or procedural deficiency. Analyzing root causes is essential for effective problem-solving. It helps identify recurring systemic issues that need to be addressed through corrective and preventive actions. This attribute directly supports the 'Root Cause Analysis Cycle Time' and 'CAPA Effectiveness' dashboards. Why it matters Categorizes the fundamental reasons for quality issues, enabling strategic analysis to prevent future recurrence. Where to get This is typically a text field or a picklist on the Quality Event object or a related Root Cause Analysis object. Examples Equipment MalfunctionProcedural ErrorInadequate TrainingMaterial Defect
	Site Site	The manufacturing site, plant, or location where the quality event occurred or was identified.
Description This attribute specifies the physical location, such as a manufacturing plant or laboratory, associated with the quality event. It provides a geographical or organizational context for the issue. This is a powerful dimension for comparative analysis, allowing management to compare process performance, issue types, and resolution times across different sites. It can help identify site-specific problems or share best practices from high-performing locations. Why it matters Provides a geographical or organizational dimension for analysis, helping to compare performance and identify site-specific issues. Where to get This is often a standard field on the Quality Event object, linking to a list of company sites or locations. Examples Site A - New JerseySite B - IrelandSite C - Switzerland

Required Recommended Optional

Quality Management Activities

These are the essential process steps and milestones to capture in your event log for accurate discovery and analysis of Quality Management.

7 Recommended 6 Optional

	Activity	Description
	Corrective Action Implemented	Represents the completion of all tasks defined in the approved corrective action plan. This activity confirms that the necessary actions have been taken to address the root cause. This is captured when the CAPA record's status is updated to 'Implementation Complete'.
Why it matters This is a major milestone that signifies the transition from planning to execution. The time between this and CAPA approval reveals the efficiency of the implementation phase. Where to get Inferred from a lifecycle state change on the associated CAPA object to a status like 'Implemented' or 'Pending Effectiveness Check'. Capture From the state change timestamp on the linked CAPA object. Event type inferred
	Corrective Action Plan Approved	Marks the formal approval of the proposed corrective action plan by relevant stakeholders, such as a quality review board. This is a critical gateway before any corrective actions can be implemented. It is captured when the CAPA plan record moves to an 'Approved' state.
Why it matters This approval step is often a major bottleneck. Measuring the 'Approval Workflow Cycle Time' for this activity helps identify and address delays in the review process, accelerating overall resolution. Where to get Inferred from the timestamp of a lifecycle state change to 'Approved' on the associated CAPA Plan object. This event may be mirrored as a state change on the parent Quality Event. Capture From the state change timestamp on the linked CAPA Plan object. Event type inferred
	Effectiveness Of Action Verified	This activity confirms that the implemented corrective actions were successful in preventing a recurrence of the issue. A formal verification is completed and documented. It is captured when the CAPA or Quality Event status is updated to 'Effectiveness Verified'.
Why it matters This is the final validation of the solution's success and is critical for calculating the 'CAPA Effectiveness Rate'. A failure at this stage often leads to rework, re-opening the investigation. Where to get Inferred from a lifecycle state change on the Quality Event or CAPA object to a status such as 'Verified' or 'Closed - Effective'. Capture From state change timestamp indicating successful verification. Event type inferred
	Final Review And Closure	Represents the last administrative review of the Quality Event record before it is officially closed. This step ensures all documentation is complete and all process steps were followed. This activity is the final step before the case is considered resolved.
Why it matters This end activity is essential for calculating the total 'Quality Event Cycle Time'. It signifies the completion of all work, and its timestamp is compared against the 'Target Resolution Date' for performance measurement. Where to get Inferred from a lifecycle state change on the Quality Event object to 'Closed', 'Resolved', or a similar terminal state. Capture From the timestamp of the final state change to 'Closed'. Event type inferred
	Investigation Initiated	Marks the formal start of the investigation into the quality event's root cause. An investigator or team is typically assigned, and the event moves into an active investigative phase. This is captured when the Quality Event's lifecycle state changes to 'Under Investigation'.
Why it matters This is a key milestone for measuring the 'Issue Identification-to-Investigation Time' KPI. Delays before this point indicate backlogs in assigning resources or starting critical analysis. Where to get Inferred from the timestamp when the Quality Event object's lifecycle state is updated to 'Under Investigation' or a similar status. The assignment of an investigator can also serve as a trigger. Capture Use the timestamp of the state change to 'Under Investigation'. Event type inferred
	Quality Event Created	This is the starting point of the process, representing the formal creation of a Quality Event record in Veeva. This activity is typically triggered when a new quality issue, non-conformance, or complaint is identified and logged into the system.
Why it matters This activity marks the beginning of the case lifecycle, making it essential for calculating the total Quality Event Cycle Time and measuring the duration of initial process phases, such as the time to start an investigation. Where to get This is an explicit event captured from the creation timestamp of the Quality Event object in Veeva Vault. The audit trail for the object will show the exact creation date, time, and user. Capture Use the creation timestamp of the main Quality Event record. Event type explicit
	Root Cause Analysis Performed	Represents the completion of the root cause analysis (RCA) phase, where the underlying cause of the quality event has been identified. This is often logged when the investigator completes the RCA task or moves the event to a 'Pending CAPA' state.
Why it matters This activity is crucial for measuring the 'Average Root Cause Analysis Time' KPI. Analyzing its duration helps identify bottlenecks in the analytical process and improve investigation quality. Where to get Typically inferred from a lifecycle state change on the Quality Event object, such as moving to 'RCA Complete' or 'Pending Action Plan'. It might also correspond to the completion of a specific workflow task. Capture From state change timestamp or completion of an RCA-related task. Event type inferred
	Corrective Action Plan Proposed	This activity occurs when a formal plan to correct the identified root cause is drafted and submitted for review. It often involves creating a related CAPA (Corrective and Preventive Action) record linked to the Quality Event. The event is captured when the CAPA record is created or the Quality Event status is updated.
Why it matters Tracking this step helps analyze the time taken to move from analysis to solution design. It's a key input for understanding the 'Root Cause Analysis Cycle Time' from RCA completion to CAPA proposal. Where to get This can be inferred from a lifecycle state change on the Quality Event object to 'Pending CAPA Approval', or from the creation timestamp of a linked CAPA Plan record. Capture Use creation timestamp of a linked CAPA record or a state change. Event type inferred
	Effectiveness Check Initiated	Marks the beginning of the period where the effectiveness of the implemented corrective actions is monitored. This is not a point-in-time event but rather the start of a verification phase. It is captured when the Quality Event or CAPA record enters a 'Monitoring' or 'Pending Effectiveness Verification' state.
Why it matters This activity initiates the final validation loop. The duration of the effectiveness check phase is important for understanding how long it takes to confirm a resolution's success. Where to get Inferred from a lifecycle state change on the Quality Event or CAPA object to a status like 'Under Effectiveness Review'. Capture From state change timestamp to a monitoring-related state. Event type inferred
	Initial Triage Completed	Represents the completion of the initial review and assessment of the quality event. During this stage, basic information is gathered and verified to determine the event's validity and immediate impact. This is often captured when the record's status changes from 'New' to 'Under Assessment' or a similar state.
Why it matters Analyzing the time spent in triage helps identify delays in the initial handling of quality events. It provides insights into workload and resource allocation at the very beginning of the process. Where to get Inferred from the timestamp when the Quality Event object's lifecycle state is updated to reflect that triage is complete, for example, moving from 'New' to 'In Assessment' or 'Triaged'. Capture From state change timestamp on the Quality Event object. Event type inferred
	Issue Categorized And Prioritized	This activity marks the point where the quality event has been formally categorized by type, severity level, and priority. This is a critical step that determines the subsequent investigation path and timeline. It's typically captured by a status change indicating the completion of this classification.
Why it matters This event is crucial for segmenting process analysis by event severity or type. It helps understand if high-priority issues are processed faster than low-priority ones and ensures resources are allocated effectively. Where to get Inferred from a lifecycle state change on the Quality Event object, such as moving to a state like 'Categorized' or 'Pending Investigation'. It can also be inferred from the timestamp when key classification fields are populated. Capture Inferred from a state change or population of fields like 'Severity' and 'Priority'. Event type inferred
	Preventive Action Identified	This activity occurs when a preventive action is identified to address potential future recurrences, often as a result of the investigation. It is captured by the creation of a Preventive Action (PA) record linked to the quality event.
Why it matters While not part of every case, tracking this activity helps understand how proactive the quality process is. It provides insights into the organization's focus on long-term prevention versus immediate correction. Where to get Inferred from the creation timestamp of a linked Preventive Action record within Veeva Vault. Capture Use creation timestamp of a linked Preventive Action record. Event type inferred
	Stakeholders Notified Of Resolution	This activity represents the communication sent to relevant stakeholders informing them of the quality event's resolution. This might be an automated email notification triggered by the final closure step or a manually logged communication task.
Why it matters Timely communication is key to stakeholder satisfaction and transparency. This activity allows for measuring the 'Stakeholder Notification Lag Time' KPI, highlighting delays in communication. Where to get This may be an explicit event if Veeva Vault sends automated notifications that are logged. Alternatively, it could be inferred from the completion of a manual 'Notify Stakeholders' workflow task. Capture From system notification logs or the completion timestamp of a communication task. Event type explicit

Recommended Optional

Extraction Guides

How to get your data from Veeva Vault Quality

Steps

Prerequisites: Ensure you have Python 3.6+ installed along with the requests and pandas libraries. You can install them using pip: pip install requests pandas. You will also need API credentials for your Veeva Vault instance, including the Vault DNS (e.g., yourcompany.veevavault.com), a username, and a password for a user with API access and read permissions on the Quality Event and related objects.
Configure the Script: Copy the provided Python script into a file named extract_quality_events.py. Open the file and update the placeholder variables at the top: VAULT_DNS, USERNAME, and PASSWORD with your specific Veeva Vault credentials.
Set the Date Range: Locate the VQL queries within the script. Each query contains a WHERE clause with a placeholder like created_date__v > '2023-01-01T00:00:00.000Z'. Modify this date to define the time window for your data extraction. It is recommended to apply this filter to a primary date field to limit the initial data pull.
Review Object and Field Names: Veeva Vault configurations can vary. The provided script uses common API names for objects (quality_event__v, capa__v) and fields (status__v, severity_level__c). Verify these names against your Vault's configuration by using the Vault API documentation or tools like the Vault Field and Object Reference. Adjust the names in the VQL queries as needed.
Execute the Script: Open a terminal or command prompt, navigate to the directory where you saved the script, and run it using the command: python extract_quality_events.py. The script will authenticate, execute a series of queries, and process the data.
Monitor Execution: The script will print status messages to the console, indicating which activity it is currently extracting and the number of records found. This helps monitor progress and identify any potential issues, such as authentication failures or empty query results.
Retrieve the Output File: Upon successful completion, the script will generate a CSV file named quality_event_log.csv in the same directory. This file contains the extracted event log.
Data Transformation and Enrichment: The script performs basic data transformation by mapping source fields to the required event log columns. It calculates the LastDataUpdate timestamp and sets a static SourceSystem value. If additional calculations are needed, such as ActivityDuration, you can modify the pandas DataFrame manipulation section before the final CSV export.
Prepare for Upload: Open the quality_event_log.csv file in a spreadsheet tool to review it. Ensure the columns QualityEvent, ActivityName, and EventTime are correctly populated. This file is now formatted for upload into process mining software.

Configuration

API Credentials: A dedicated API user account with a 'Security Policy' that allows API access is required. The user's 'Permission Set' must grant at least 'Read' access to the quality_event__v object, all fields being queried, and any related objects like capa__v.
Vault DNS: The script requires the specific DNS for your Vault instance, for example, yourcompany.veevavault.com. This is used to construct the API endpoint URLs.
Date Range Filtering: To manage the volume of extracted data, it is crucial to apply a date filter in the WHERE clause of each VQL query. We recommend starting with a recent 3 to 6 month period (e.g., created_date__v > 'YYYY-MM-DD') to ensure manageable performance and faster testing cycles.
Object and Field API Names: All VQL queries must use the system's internal API names, which may differ from the labels seen in the user interface. For custom fields, they typically end in __c. Verify all object and field names in your Vault's configuration.
Pagination Handling: The script is designed to handle Veeva's API pagination automatically. It repeatedly queries for the 'next_page' URL provided by the API until all results for a given VQL query are retrieved. No manual configuration is needed for this.
Performance Considerations: Extracting data for a long time period or from a Vault with millions of Quality Events can be time-consuming and may hit API rate limits. Run the extraction during off-peak hours and use restrictive date filters to minimize impact.

a Sample Query python

import requests
import pandas as pd
from datetime import datetime
import io

class=class="hl-string">"hl-comment"># --- Configuration: Update these values --- #
VAULT_DNS = class="hl-string">"[your_vault_dns].veevavault.com"  class=class="hl-string">"hl-comment"># Example: yourcompany.veevavault.com
USERNAME = class="hl-string">"[your_api_username]"
PASSWORD = class="hl-string">"[your_api_password]"
API_VERSION = class="hl-string">"v23.1"
OUTPUT_FILENAME = class="hl-string">"quality_event_log.csv"
class=class="hl-string">"hl-comment"># --- End of Configuration --- #

def get_session_id():
    class="hl-string">"""Authenticates with Veeva Vault API and returns a session ID."""
    auth_url = fclass="hl-string">"https://{VAULT_DNS}/api/{API_VERSION}/auth"
    headers = {class="hl-string">"Content-Type": class="hl-string">"application/x-www-form-urlencoded", class="hl-string">"Accept": class="hl-string">"application/json"}
    data = {class="hl-string">"username": USERNAME, class="hl-string">"password": PASSWORD}
    
    try:
        response = requests.post(auth_url, headers=headers, data=data)
        response.raise_for_status() class=class="hl-string">"hl-comment"># Raises an exception for bad responses (4xx or 5xx)
        auth_data = response.json()
        if auth_data.get(class="hl-string">"responseStatus") == class="hl-string">"SUCCESS":
            print(class="hl-string">"Authentication successful.")
            return auth_data[class="hl-string">"sessionId"]
        else:
            print(fclass="hl-string">"Authentication failed: {auth_data.get('responseMessage')}")
            return None
    except requests.exceptions.RequestException as e:
        print(fclass="hl-string">"An error occurred during authentication: {e}")
        return None

def run_vql_query(session_id, query):
    class="hl-string">"""Runs a VQL query, handles pagination, and returns all results as a pandas DataFrame."""
    query_url = fclass="hl-string">"https://{VAULT_DNS}/api/{API_VERSION}/query"
    headers = {class="hl-string">"Authorization": session_id, class="hl-string">"Accept": class="hl-string">"application/json"}
    all_results = []
    
    params = {class="hl-string">'q': query}
    
    page_num = 1
    while True:
        print(fclass="hl-string">"Fetching page {page_num}...")
        try:
            response = requests.post(query_url, headers=headers, params=params)
            response.raise_for_status()
            query_response = response.json()
            
            if query_response.get(class="hl-string">"responseStatus") != class="hl-string">"SUCCESS":
                print(fclass="hl-string">"Query failed: {query_response.get('responseDetails', {}).get('message')}")
                break

            all_results.extend(query_response.get(class="hl-string">'data', []))
            
            if class="hl-string">'next_page' in query_response.get(class="hl-string">'responseDetails', {}):
                next_page_url = query_response[class="hl-string">'responseDetails'][class="hl-string">'next_page']
                query_url = fclass="hl-string">"https://{VAULT_DNS}{next_page_url}"
                params = {}
                page_num += 1
            else:
                break
        except requests.exceptions.RequestException as e:
            print(fclass="hl-string">"An error occurred during query execution: {e}")
            break

    if not all_results:
        return pd.DataFrame()
    
    class=class="hl-string">"hl-comment"># Convert list of dictionaries to DataFrame
    class=class="hl-string">"hl-comment"># Veeva API returns data as {class="hl-string">'field_name': class="hl-string">'value'}. We need to flatten this.
    flat_data = [{key: item[key] for key in item} for item in all_results]
    return pd.DataFrame(flat_data)


def main():
    session_id = get_session_id()
    if not session_id:
        return

    class=class="hl-string">"hl-comment"># Common fields to select in every query for context
    common_fields = (
        class="hl-string">"id, "
        class="hl-string">"name__v, "
        class="hl-string">"investigator__c, "
        class="hl-string">"department_owner__c, "
        class="hl-string">"type_of_quality_event__c, "
        class="hl-string">"status__v, "
        class="hl-string">"target_due_date__c, "
        class="hl-string">"severity__c"
    )

    class=class="hl-string">"hl-comment"># Define VQL queries for each activity
    class=class="hl-string">"hl-comment"># NOTE: Field names like class="hl-string">'triage_completed_date__c' are placeholders for common custom date fields.
    class=class="hl-string">"hl-comment"># Adjust these to match your Veeva Vault configuration.
    activity_queries = {
        class="hl-string">"Quality Event Created": fclass="hl-string">"SELECT {common_fields}, created_date__v FROM quality_event__v WHERE created_date__v > '2023-01-01T00:00:00.000Z'",
        class="hl-string">"Initial Triage Completed": fclass="hl-string">"SELECT {common_fields}, triage_completed_date__c FROM quality_event__v WHERE triage_completed_date__c != null",
        class="hl-string">"Issue Categorized And Prioritized": fclass="hl-string">"SELECT {common_fields}, categorization_date__c FROM quality_event__v WHERE categorization_date__c != null",
        class="hl-string">"Investigation Initiated": fclass="hl-string">"SELECT {common_fields}, investigation_start_date__c FROM quality_event__v WHERE investigation_start_date__c != null",
        class="hl-string">"Root Cause Analysis Performed": fclass="hl-string">"SELECT {common_fields}, rca_completed_date__c FROM quality_event__v WHERE rca_completed_date__c != null",
        class="hl-string">"Corrective Action Plan Proposed": fclass="hl-string">"SELECT {common_fields}, quality_event__vr.created_date__v FROM capa__v WHERE quality_event__vr.created_date__v != null AND action_type__c = 'Corrective'",
        class="hl-string">"Corrective Action Plan Approved": fclass="hl-string">"SELECT {common_fields}, quality_event__vr.approval_date__c FROM capa__v WHERE quality_event__vr.approval_date__c != null AND action_type__c = 'Corrective'",
        class="hl-string">"Corrective Action Implemented": fclass="hl-string">"SELECT {common_fields}, quality_event__vr.implementation_date__c FROM capa__v WHERE quality_event__vr.implementation_date__c != null AND action_type__c = 'Corrective'",
        class="hl-string">"Preventive Action Identified": fclass="hl-string">"SELECT {common_fields}, quality_event__vr.created_date__v FROM capa__v WHERE quality_event__vr.created_date__v != null AND action_type__c = 'Preventive'",
        class="hl-string">"Effectiveness Check Initiated": fclass="hl-string">"SELECT {common_fields}, effectiveness_check_start_date__c FROM quality_event__v WHERE effectiveness_check_start_date__c != null",
        class="hl-string">"Effectiveness Of Action Verified": fclass="hl-string">"SELECT {common_fields}, effectiveness_verified_date__c FROM quality_event__v WHERE effectiveness_verified_date__c != null",
        class="hl-string">"Final Review And Closure": fclass="hl-string">"SELECT {common_fields}, closed_date__c FROM quality_event__v WHERE closed_date__c != null",
        class="hl-string">"Stakeholders Notified Of Resolution": fclass="hl-string">"SELECT {common_fields}, notification_date__c FROM quality_event__v WHERE notification_date__c != null"
    }

    all_events_df = pd.DataFrame()

    for activity_name, query in activity_queries.items():
        print(fclass="hl-string">"\n--- Extracting activity: {activity_name} ---")
        df = run_vql_query(session_id, query)
        
        if df.empty:
            print(fclass="hl-string">"No data found for activity: {activity_name}")
            continue

        print(fclass="hl-string">"Found {len(df)} records for {activity_name}")
        
        class=class="hl-string">"hl-comment"># Standardize columns
        df[class="hl-string">'ActivityName'] = activity_name
        df[class="hl-string">'SourceSystem'] = class="hl-string">'Veeva Vault Quality'
        df[class="hl-string">'LastDataUpdate'] = datetime.utcnow().isoformat() + class="hl-string">'Z'
        
        class=class="hl-string">"hl-comment"># Find the correct timestamp column based on the activity name
        class=class="hl-string">"hl-comment"># This logic assumes the last field in the SELECT statement is the event time
        timestamp_field = query.split(class="hl-string">' FROM')[0].split(class="hl-string">',')[-1].strip()
        if class="hl-string">'.' in timestamp_field:
            timestamp_field = timestamp_field.split(class="hl-string">'.')[-1]

        df.rename(columns={
            class="hl-string">'name__v': class="hl-string">'QualityEvent',
            timestamp_field: class="hl-string">'EventTime',
            class="hl-string">'investigator__c': class="hl-string">'AssignedInvestigator',
            class="hl-string">'department_owner__c': class="hl-string">'AssignedDepartment',
            class="hl-string">'type_of_quality_event__c': class="hl-string">'QualityEventType',
            class="hl-string">'status__v': class="hl-string">'QualityEventStatus',
            class="hl-string">'target_due_date__c': class="hl-string">'TargetResolutionDate',
            class="hl-string">'severity__c': class="hl-string">'SeverityLevel'
        }, inplace=True)
        
        class=class="hl-string">"hl-comment"># For CAPA queries, the Quality Event name is nested
        if class="hl-string">'quality_event__vr.name__v' in df.columns:
            df.rename(columns={class="hl-string">'quality_event__vr.name__v': class="hl-string">'QualityEvent'}, inplace=True)

        all_events_df = pd.concat([all_events_df, df], ignore_index=True)
    
    class=class="hl-string">"hl-comment"># Define the final column structure
    final_columns = [
        class="hl-string">'QualityEvent', class="hl-string">'ActivityName', class="hl-string">'EventTime', class="hl-string">'SourceSystem', class="hl-string">'LastDataUpdate',
        class="hl-string">'AssignedInvestigator', class="hl-string">'AssignedDepartment', class="hl-string">'QualityEventType',
        class="hl-string">'QualityEventStatus', class="hl-string">'TargetResolutionDate', class="hl-string">'SeverityLevel'
    ]
    
    class=class="hl-string">"hl-comment"># Select and reorder columns, filling missing ones with None
    output_df = pd.DataFrame(columns=final_columns)
    for col in final_columns:
        if col in all_events_df.columns:
            output_df[col] = all_events_df[col]
        else:
            output_df[col] = None
    
    class=class="hl-string">"hl-comment"># Drop rows where EventTime is NaT (Not a Time)
    output_df.dropna(subset=[class="hl-string">'EventTime'], inplace=True)

    class=class="hl-string">"hl-comment"># Sort by case and event time for readability
    output_df.sort_values(by=[class="hl-string">'QualityEvent', class="hl-string">'EventTime'], inplace=True)
    
    output_df.to_csv(OUTPUT_FILENAME, index=False)
    print(fclass="hl-string">"\nExtraction complete. Event log saved to {OUTPUT_FILENAME}")

if __name__ == class="hl-string">"__main__":
    main()

Steps

Install and Configure Veeva Vault Loader: Download the Veeva Vault Loader command-line tool from your Vault's Apps tab. Unzip the file to a dedicated directory. Ensure you have Java 1.8 or higher installed on the machine where you will run the extraction.
Prepare the Configuration File: Create a CSV file to define the export query. This file will contain a single entry with the VQL query. For example, create a file named vql_query.csv.
Construct the VQL Query: Open vql_query.csv and add a header row query. In the second row, add the complete VQL query provided in the query section below. This query is designed to extract all necessary fields from the Quality Event object and related records to build the event log.
Create the Bash Script: Create a bash script file, for example run_export.sh, to automate the execution of the Vault Loader. Copy the script from the query section into this file. This script will handle authentication and execute the export.
Set Script Variables: Edit the run_export.sh script and replace all placeholder values. This includes your Vault DNS, username, password, the path to the vault-loader.jar file, and the paths for input and output files.
Execute the Extraction: Open a terminal or command prompt, navigate to the directory containing your script, and run it using the command: bash run_export.sh. The script will connect to Veeva Vault, execute the VQL query, and download the results into a CSV file named quality_events_raw.csv.
Transform the Raw Data: The output quality_events_raw.csv is a wide-format table with one row per Quality Event. This data must be transformed into a long-format event log. Use a data preparation tool or script (like Python or an ETL tool) to pivot the various date columns into separate rows. For each Quality Event, create a row for each non-empty date field, mapping it to the corresponding ActivityName.
Map to Event Log Schema: During the transformation, map the columns from the raw extract to the required event log schema. For example, the id column becomes QualityEvent, and each pivoted date column becomes the EventTime. Add a SourceSystem column with a static value like 'Veeva Vault Quality' and a LastDataUpdate column with the current timestamp.
Final Output Generation: Save the transformed data as the final event log CSV file, ready for upload into ProcessMind. Ensure the file encoding is UTF-8 and the headers match the required attributes.

Configuration

Prerequisites: You must have a Veeva Vault user account with API access and sufficient permissions to read the Quality Event object (quality_event__v) and any related objects like CAPA (capa__v). The Veeva Vault Loader command-line tool must be installed.
Authentication: The script uses a username and password for authentication. Ensure the account used has a security profile that allows for API interaction.
Data Scope: The VQL query includes a WHERE clause to filter the data. It is critical to configure the created_date__v filter to limit the extraction to a relevant time period, such as the last 6 to 12 months, to manage file size and performance.
Object and Field Selection: The provided query assumes standard object names like quality_event__v and capa__v. You must verify these names and the custom field names, like triage_completion_date__c, in your specific Veeva Vault configuration and adjust the query accordingly.
Output Location: The script defines an OUTPUTDIR. Ensure the user running the script has write permissions to this directory, as this is where the extracted CSV file will be saved.
Scheduling: For automation, this bash script can be scheduled using a cron job (on Linux) or a similar scheduling tool to run at regular intervals, such as daily or weekly during off-peak hours.

a Sample Query bash

#!/bin/bash

# Veeva Vault Connection Details (Replace with your credentials)
VAULTDNS="[Your Vault DNS e.g., your-vault.veevavault.com]"
USERNAME="[Your Username]"
PASSWORD="[Your Password]"

# Configuration Paths (Update paths as needed)
LOADERDIR="/path/to/vault-loader"
INPUTDIR="/path/to/input/files"
OUTPUTDIR="/path/to/output/files"
LOGFILE="${OUTPUTDIR}/loader_export.log"

# Input VQL Query File
QUERYCSV="${INPUTDIR}/vql_query.csv"

# Raw Output File
OUTPUTCSV="${OUTPUTDIR}/quality_events_raw.csv"

# --- VQL Query Definition ---
# This query extracts a wide table of Quality Event data.
# A separate transformation step is required to pivot this into an event log.
# Field names (`__c`) are examples and must be verified in your Vault.
cat > "$QUERYCSV" <<- EOM
query
"SELECT id, name__v, created_date__v, status__v, type__v, severity_level__c, target_resolution_date__c, assigned_investigator__c, assigned_department__c, initial_triage_completed_date__c, issue_categorized_date__c, investigation_initiated_date__c, rca_performed_date__c, capa_proposed_date__c, capa_approved_date__c, capa_implemented_date__c, pa_identified_date__c, effectiveness_check_initiated_date__c, effectiveness_verified_date__c, final_review_closure_date__c, stakeholder_notification_date__c FROM quality_event__v WHERE created_date__v >= '2023-01-01'"
EOM

# --- Veeva Loader Execution ---
echo "Starting Veeva Vault Quality Event export..."

java -jar "${LOADERDIR}/vault-loader.jar" \
-action EXTRACT \
-V "https://www.veeva.com" \
-vql "$QUERYCSV" \
-outputdir "$OUTPUTDIR" \
-resultsfile "$OUTPUTCSV" \
-u "$USERNAME" \
-p "$PASSWORD" \
-dns "$VAULTDNS" \
-logfile "$LOGFILE"

echo "Export complete. Raw data saved to $OUTPUTCSV"
echo "Log file available at $LOGFILE"

Your Quality Management Data Template

Your Quality Management Data Template

Quality Management Attributes

Quality Management Activities

Extraction Guides

Veeva Vault API Extraction using Python Script

Steps

Configuration

a Sample Query python

Veeva Vault Loader Command-Line Bulk Data Export

Steps

Configuration

a Sample Query bash

Ready to Get Started?

Optimize Quality Management: Start Your Free Trial Today

Your Quality Management Data Template

Your Quality Management Data Template

Quality Management Attributes

Quality Management Activities

Extraction Guides

Veeva Vault API Extraction using Python Script

Steps

Configuration

a Sample Query python

Veeva Vault Loader Command-Line Bulk Data Export

Steps

Configuration

a Sample Query bash

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Ready to Get Started?

Optimize Quality Management: Start Your Free Trial Today